Developments in network technologies, scholarly communication, and national policy are challenging academic libraries to find new ways to engage with research communities in the economic downturn. Librarians are responding with service innovations in areas such as bibliometrics and research data management. Previous surveys have investigated research data support within North America and other research services globally with small samples. An online multiple-choice questionnaire was used to survey bibliometric and data support activities of 140 libraries in Australia, New Zealand, Ireland, and the United Kingdom, including current and planned services, target audiences, service constraints, and staff training needs. A majority of respondents offered or planned bibliometrics training, citation reports, and impact calculations but with significant differences between countries. Current levels of engagement in data management were lower than for bibliometrics, but a majority anticipated future involvement, especially in technology assistance, data deposit, and policy development. Initiatives were aimed at multiple constituencies, with university administrators being important clients and partners for bibliometric services. Gaps in knowledge, skills, and confidence were significant constraints, with near-universal support for including bibliometrics and particularly data management in professional education and continuing development programs. The study also found that librarians need a multilayered understanding of the research environment.

/pdf/bibliometrics-and-research-data-management-services-emerging-2nv46fu2vb.pdf

Bibliometrics and Research Data Management Services: Emerging Trends in Library Support for Research

Abstract The growing importance of data in society in general and scientific domains in particular, mirrored in the Open Data initiative and in the advent of eScience, requires public, school and academic libraries to contribute to both data and information literacy, as part of their mission to further knowledge and innovation in their respective fields of action. No specific library standards have been proposed to date, however, and most research studies conducted adopt a partial view of data literacy, stressing only the components needed in any given context. The present paper aims to contribute to the advancement of data literacy with the proposal of a set of core competencies and contents that can serve as a framework of reference for its inclusion in libraries’ information literacy programs. The various definitions of data literacy are discussed, the coverage of the competencies listed in information literacy standards is described, and the competencies considered in the experiments conducted to date in education and libraries are identified. The conclusion drawn is that the model proposed can favour the development of data literacy support resources and services. Topics for further research are also specified.

/pdf/incorporating-data-literacy-into-information-literacy-2holoxvfai.pdf

Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents

As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance, a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.

Constance: An Intelligent Data Lake System

Openness is one of the central values of science. Open scientific practices such as sharing data, materials and analysis scripts alongside published articles have many benefits, including easier replication and extension studies, increased availability of data for theory-building and meta-analysis, and increased possibility of review and collaboration even after a paper has been published. Although modern information technology makes sharing easier than ever before, uptake of open practices had been slow. We suggest this might be in part due to a social dilemma arising from misaligned incentives and propose a specific, concrete mechanism—reviewers withholding comprehensive review—to achieve the goal of creating the expectation of open practices as a matter of scientific principle.

/pdf/the-peer-reviewers-openness-initiative-incentivizing-open-76yfo9in6x.pdf

The Peer Reviewers' Openness Initiative: incentivizing open research practices through peer review

IoT survey: An SDN and fog computing perspective

This paper presents Personal Data Lake, a unified storage facility for storing, analyzing and querying personal data. A data lake stores data regardless of format and thus provides an intuitive way to store personal data fragments of any type. Metadata management is a central part of the lake architecture. For structured/semi-structured data fragments, metadata may contain information about the schema of the data so that the data can be transformed into queryable data objects when required. For unstructured data, enabling gravity pull means allowing third-party plugins so that the unstructured data can be analyzed and queried.

Personal Data Lake with Data Gravity Pull

Utility Computing requires levels of efficacy and abstraction that are not matched by modern computing clouds. In this paper we argue that the lack of harmonization of data and computation is holding back computing clouds from evolving further into utility clouds. Data gravity is perceived as gravity pull associated with data volume. Data of large volume develop large gravity that pulls computation to it, which contrasts with the computation-centered principle of current computing clouds, where computational resources are centralized and fixed. Based on previous research, eight principles are identified to suggest an infrastructure for a future data-driven utility computing platform. The paper also argues that tuple spaces and functional programming languages together would be a powerful combination to meet the requirements for this next-generation computing utility.

The Problem with Data

Keyphrases provide important semantic metadata for organizing and managing free-text documents. As data grow exponentially, there is a pressing demand for automatic and efficient keyphrase extraction methods. We introduce in this paper SemCluster, a clustering-based unsupervised keyphrase extraction method. By integrating an internal ontology (i.e., WordNet) with external knowledge sources, SemCluster identifies and extracts semantically important terms from a given document, clusters the terms, and, using the clustering results as heuristics, identifies the most representative phrases and singles them out as keyphrases. SemCluster is evaluated against two baseline unsupervised methods, TextRank and KeyCluster, over the Inspec dataset under an F1-measure metric. The evaluation results clearly show that SemCluster outperforms both methods.

SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation

Keyphrases are single- or multi-word phrases that are used to describe the essential content of a document. Utilizing an external knowledge source such as WordNet is often used in keyphrase extraction methods to obtain relation information about terms and thus improves the result, but the drawback is that a sole knowledge source is often limited. This problem is identified as the coverage limitation problem. In this paper, we introduce SemCluster, a clustering-based unsupervised keyphrase extraction method that addresses the coverage limitation problem by using an extensible approach that integrates an internal ontology (i.e., WordNet) with other knowledge sources to gain a wider background knowledge. SemCluster is evaluated against three unsupervised methods, TextRank, ExpandRank, and KeyCluster, and under the F1-measure metric. The evaluation results demonstrate that SemCluster has better accuracy and computational efficiency and is more robust when dealing with documents from different domains.

/pdf/exploiting-extensible-background-knowledge-for-clustering-4rzlmum8pn.pdf

Exploiting extensible background knowledge for clustering-based automatic keyphrase extraction

A data integration approach combines data from different sources and builds a unified view for the users. Big data integration inherently is a complex task, and the existing approaches are either potentially limited or invariably rely on manual inputs and interposition from experts or skilled users. SemLinker, an ontology-based data integration system, is part of a metadata management framework for personal data lake (PDL), a personal store-everything architecture. PDL is for casual and unskilled users, therefore SemLinker adopts an automated data integration workflow to minimize manual input requirements. To support the flat architecture of a lake, SemLinker builds and maintains a schema metadata level without involving any physical transformation of data during integration, preserving the data in their native formats while, at the same time, allowing them to be queried and analyzed. Scalability, heterogeneity, and schema evolution are big data integration challenges that are addressed by SemLinker. Large and real-world datasets of substantial heterogeneities are used in evaluating SemLinker. The results demonstrate and confirm the integration efficiency and robustness of SemLinker, especially regarding its capability in the automatic handling of data heterogeneities and schema evolutions.

/pdf/semlinker-automating-big-data-integration-for-casual-users-417oz1tyts.pdf

Coral Walker

Papers

Personal Data Lake with Data Gravity Pull

The Problem with Data

SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation

Exploiting extensible background knowledge for clustering-based automatic keyphrase extraction

SemLinker: automating big data integration for casual users